Optimal Key Generation for Data Sanitization and Restoration of Cloud Data: Future of Financial Cyber Security

Abstract

Cloud security in finance is considered as the key importance, taking account of the aspect of critical data stored over cloud spaces within organizations all around the globe. They are chiefly relying on cloud computing to accelerate their business profitability and scale up their business processes with enhanced productivity coming through flexible work environments offered in cloud-run working systems. Hence, there is a prerequisite to contemplate cloud security in the entire financial service sector. Moreover, the main issue challenged by privacy and security is the presence of diverse chances to attack the sensitive data by cloud operators, which leads to double the user’s anxiety on the stored data. For solving this problem, the main intent of this paper is to develop an intelligent privacy preservation approach for data stored in the cloud sector, mainly the financial data. The proposed privacy preservation model involves two main phases: (a) data sanitization and (b) data restoration. In the sanitization process, the sensitive data is hidden, which prevents sensitive information from leaking on the cloud side. Further, the normal as well as the sensitive data is stored in a cloud environment. For the sanitization process, a key should be generated that depends on the new meta-heuristic algorithm called crossover improved-lion algorithm (CI-LA), which is inspired by the lion’s unique social behavior. During data restoration, the same key should be used for effectively restoring the original data. Here, the optimal key generation is done in such a way that the objective model involves the degree of modification, hiding rate, and information preservation rate, which effectively enhance the cyber security performance in the cloud.

Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework

“…Therefore, this research determined certain important problems stated above in the previous study 12 , 14 , 17 , 18 , 24 , which it resolved and turned into the main subject of this research contributions to the field. These crucial concerns comprise, but are not restricted to, the following: Which value will determine how long the key is?…”

“…Some studies are emphasizing data sanitization process using optimized key, whereas only a few studies have proposed a privacy-preserving restoration procedure for autism data with limited performance accuracies 12 – 23 . Data restoration refers to the retrieval of backup data from secondary storage and its reinstatement to its initial or an alternative location 24 . The procedure is crucial in guaranteeing the effective recovery of genuine data and promoting computer network development.…”

Enhancement of an Optimized Key for Database Sanitization to Ensure the Security and Privacy of an Autism Dataset

“…For example, in work [22], Alphonsa MMA et al developed a secure model named GMGW to sanitize sensitive information of heart disease data based on the cloud system. In the same way, the authors in [24,25,28] developed security and privacy models individually by applying different algorithms to the cloud computing system for data security and compared the performance of their models with the conventional algorithms for more improvement.…”

“…In addition, some of these traditional meta-heuristics algorithms also possess lower solving precision, slower convergence, and worse local searching ability. Moreover, we identified certain critical issues in the existing studies [5,6,[24][25][26] which we addressed, thus forming the focus of our research contributions. Such critical issues, put in question form, include, but are not limited to, the following:…”